Results 161 to 170 of about 133,461 (264)

Machine Learning and the Use of Spectroscopy for Adulteration Detection in Turmeric Powder. [PDF]

Molecules
Kisalaei A, Sharabiani VR, Banakar A, Taghinezhad E, Szymanek M, Dziwulska-Hunek A.   +5 more
europepmc   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Mumtathil: Automatic PDPL compliance identification system of Arabic privacy policies documents. [PDF]

PLoS One
Mashaabi M, Al-Khalifa H.
europepmc   +1 more source

Explainable hybrid stacking ensemble method for hard rock pillar stability prediction and engineering applications

Deep Underground Science and Engineering, EarlyView.
This research proposes an interpretable hybrid stacking ensemble framework, optimized by the Sparrow Search Algorithm, to enhance hard rock pillar stability prediction. By integrating six machine learning models—k‐nearest neighbors, support vector machines, random forests, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and Light Gradient ...
Ning Wang, Guoyan Zhao, Meng Wang, Taraka Pattela   +3 more
wiley   +1 more source

A systematic review of machine learning on clinical MALDI-TOF MS. [PDF]

Brief Bioinform
Schmidt-Santiago L, Guerrero-López A, Sevilla-Salcedo C, Rodríguez-Temporal D, Rodríguez-Sánchez B, Gómez-Verdejo V.   +5 more
europepmc   +1 more source

Machine Learning‐Assisted Design of BaTiO3‐Based Superparaelectric High‐Entropy Ceramics with Superior Energy Storage

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This study employed an adaptive iterative strategy combining machine learning algorithms, domain knowledge, experimental design, and experimental feedback to aim to precisely and quickly discover high‐entropy ceramics with excellent energy storage performance.
Haowen Liu, Xiaoyan Zhang, Zhiyuan Ma, Kailong Ma, Xiwei Qi   +4 more
wiley   +1 more source

A Novel Machine-Learning Based Method for Resolving Secondary Structure Topology in Medium-Resolution Cryo-EM Density Maps. [PDF]

Int J Mol Sci
Behkamal B, Etemadheravi MP, Mahmoodjanloo A, Mansoori A, Naghibzadeh M, Al Nasr K, Saberi MR.   +6 more
europepmc   +1 more source

A metric learning perspective of SVM: on the relation of LMNN and SVM.

Journal of Machine Learning Research - Proceedings Track, 2012
Do Huyen, Kalousis Alexandros, Wang Jun, Woznica Adam   +3 more
openaire   +2 more sources

Lignocellulosic Triboelectric Materials for Energy Harvesting and Emerging Applications: A Review on Lignin‐Molecular Engineering toward Improved Performance

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Contrasting sustainable biomass with depleting petroleum resources, this review charts the evolutionary timeline of lignin‐based TENGs. We systematically evaluate their fundamental mechanisms, enhancement strategies, and applications in green self‐powered electronics.
Yuxin Yang, Boyu Du, Yuwen Wang, Tian Tang, Yuxuan Xie, Jiajun Zhang, Yajun Zhang, Jingyu Xu, Xing Wang, Chuanyin Xiong   +9 more
wiley   +1 more source

Bio-Inspired Deep Learning for Parkinson's Disease Detection: A Comparative Study Based on Vocal Biomarkers and Archimedean Spiral Analysis. [PDF]

Biomimetics (Basel)
Stan OP, Misaros M, Miclea LC.
europepmc   +1 more source